Associative memory is the important function performed by neural networks (NN), often used in image recognition [1]. Recent advances in creating artificial neural networks (ANN) have lead to numerous proposals for implementing such memory function. These largely focus on software based algorithmic methods and VLSI designs, but also on idealized physical systems such as Ising spin chains and spin glasses [2]. A detailed physical design of an associative memory based on a magnetically responsive layer, a thin layer of spin glass, where the hidden synaptic weights are represented by inter-spin exchange interactions, has been proposed [3, 4]. It is well known, however, that the spin glass behaviour is lost already by room temperature, so the oscillatory exchange interactions found at the core of the proposed memory principle are not present at the operating temperatures. Rather high operating temperatures needed for achieving a mechanical motion of the magnetic atoms in the spin glass film during training (typically of the order of the melting point of the metal, ≈1000K) far exceed the typical spin glass transition temperatures (typically 1-100 K) above which there can be no input-output magnetic correlations.